DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yuan, Yuan | ko |
dc.contributor.author | Guan, Muzhi | ko |
dc.contributor.author | Zhou, Zhilun | ko |
dc.contributor.author | Kim, Sundong | ko |
dc.contributor.author | Cha, Meeyoung | ko |
dc.contributor.author | Jin, Depeng | ko |
dc.contributor.author | Li, Yong | ko |
dc.date.accessioned | 2021-10-31T06:43:44Z | - |
dc.date.available | 2021-10-31T06:43:44Z | - |
dc.date.created | 2021-10-31 | - |
dc.date.created | 2021-10-31 | - |
dc.date.created | 2021-10-31 | - |
dc.date.created | 2021-10-31 | - |
dc.date.issued | 2021-03 | - |
dc.identifier.citation | FRONTIERS IN COMPUTER SCIENCE, v.3 | - |
dc.identifier.issn | 2624-9898 | - |
dc.identifier.uri | http://hdl.handle.net/10203/288483 | - |
dc.description.abstract | The recent outbreak of the novel coronavirus (COVID-19) has infected millions of citizens worldwide and claimed many lives. This paper examines the impact of COVID-19 on Chinese e-commerce by analyzing behavioral changes observed on a large online shopping platform. We first conduct a time series analysis to identify product categories that faced the most extensive disruptions. The time-lagged analysis shows that behavioral patterns of shopping actions are highly responsive to the epidemic's development. Based on these findings, we present a consumer demand prediction method by encompassing the epidemic statistics and behavioral features of COVID-19-related products. Experimental results demonstrate that our predictions outperform existing baselines and further extend to long-term and province-level forecasts. Finally, we discuss how our market analysis and prediction can help better prepare for future pandemics by gaining extra time to launch preventive measures.</p> | - |
dc.language | English | - |
dc.publisher | FRONTIERS MEDIA SA | - |
dc.title | Disruption in Chinese E-Commerce During COVID-19 | - |
dc.type | Article | - |
dc.identifier.scopusid | 2-s2.0-85117887265 | - |
dc.type.rims | ART | - |
dc.citation.volume | 3 | - |
dc.citation.publicationname | FRONTIERS IN COMPUTER SCIENCE | - |
dc.identifier.doi | 10.3389/fcomp.2021.668711 | - |
dc.contributor.localauthor | Cha, Meeyoung | - |
dc.contributor.nonIdAuthor | Yuan, Yuan | - |
dc.contributor.nonIdAuthor | Guan, Muzhi | - |
dc.contributor.nonIdAuthor | Zhou, Zhilun | - |
dc.contributor.nonIdAuthor | Kim, Sundong | - |
dc.contributor.nonIdAuthor | Jin, Depeng | - |
dc.contributor.nonIdAuthor | Li, Yong | - |
dc.description.isOpenAccess | Y | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | COVID19 | - |
dc.subject.keywordAuthor | disruption | - |
dc.subject.keywordAuthor | online shopping | - |
dc.subject.keywordAuthor | time-lagged analysis | - |
dc.subject.keywordAuthor | demand forecasting | - |
dc.subject.keywordPlus | ECONOMIC-IMPACT | - |
dc.subject.keywordPlus | INFLUENZA | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.